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Radioresistance—a living cell's response to, and development of resistance to ionising radiation—can lead to radiotherapy failure and/or tumour recurrence. We used Raman spectroscopy and machine learning to characterise biochemical changes that occur in acquired radioresistance for breast cancer cells. We were able to distinguish between wild-type and acquired radioresistant cells by changes in chemical composition using Raman spectroscopy and machine learning with 100% accuracy. In studying both hormone receptor positive and negative cells, we found similar changes in chemical composition that occur with the development of acquired radioresistance; these radioresistant cells contained less lipids and proteins compared to their parental counterparts. As well as characterising acquired radioresistance in vitro, this approach has the potential to be translated into a clinical setting, to look for Raman signals of radioresistance in tumours or biopsies; that would lead to tailored clinical treatments. [ABSTRACT FROM AUTHOR]

Raman spectroscopy allows nondestructive analysis of materials using laser illumination. However, most Raman spectrometers can only provide good signal levels and sufficient spectral resolution, by focusing the laser to micrometer-sized spots. This equates to enormous laser intensities, which for samples with even very minor optical absorption either means destroying or damaging it by absorbing even a tiny fraction of the laser power, or it means reducing the laser intensity and hence the signal level. Furthermore, Raman signals generated above or below the focal plane are rejected in traditional Raman spectrometers. As signal levels are already extremely low in Raman spectroscopy, several schemes offer an alternative to focusing down to a diffraction-limited spot, to increase the area by up to 6 orders of magnitude, and increase the sampling depth. This review describes and compares these schemes, and estimates the typical illumination areas. [ABSTRACT FROM AUTHOR]

We present a Raman spectroscopy case study of living fibroblast (skin) cells from a patient who developed Huntington’s disease, with fibroblasts from a healthy volunteer as a control. Spectra were processed to remove cosmic rays, had a spectrum of the quartz substrate subtracted, and were flattened to remove cellular autofluorescence. We achieved an accuracy of 95% in discriminating individual cells, and assign spectral differences to (i) the reduction of cholesterol, (ii) the reduction of lipids, and (iii) an increase in beta-sheet proteins for fibroblasts with Huntington’s disease. All these biochemical changes have been previously measured by other methods. Averages over all the cells in this study yield a difference which is extremely statistically significant [p < 0.0001]. [ABSTRACT FROM AUTHOR]

Raman spectroscopy can identify cancerous from healthy tissue, with a chemical analysis from the measurement of vibrational bond frequencies. However, to detect small tumors a form of Raman imaging is required. Such imaging—by acquiring a Raman spectrum at each imaging pixel—can detect tumors but is rather slow. Multiphoton versions of Raman—anti-Stokes Raman scattering (CARS) microscopy and stimulated Raman scattering (SRS) microscopy—offer similar accuracies in identifying cancerous tissue and tumor margins but with a far higher speed, which is beneficial for diagnosis of small tumors in tissue. SRS microscopy can also be used to image extrinsic molecules in living cells, such as anti-cancer drugs at typical concentrations. [ABSTRACT FROM PUBLISHER]

We used three label-free minimally invasive methods to characterize individual cells derived from primary and secondary tumours from the same patient, and of the same type – colorectal. Raman spectroscopy distinguished cells by their biochemical ‘fingerprint’ in a vibrational spectrum with 100% accuracy, and revealed that the primary cell line contains more lipids and alpha-helix proteins, whereas the secondary cell line contains more porphyrins and beta-sheet proteins. Stimulated Raman scattering (SRS) microscopy distinguished cells in chemically-specific images of CH2 bonds which revealed lipid droplets in secondary tumour cells. Atomic force microscopy (AFM) was used to distinguish cells with 80% accuracy by measuring their elasticity – secondary tumour cells (SW620) are around 3 times softer than primary ones (SW480). As well as characterizing the physical and biochemical differences between cell lines in vitro, these techniques offer three novel methods which could potentially be used for diagnosis – to assign a tumour as primary or secondary. [ABSTRACT FROM AUTHOR]

In this review we describe label-free optical spectroscopy techniques which are able to non-invasively measure the (bio)chemistry in biological systems. Raman spectroscopy uses visible or near-infrared light to measure a spectrum of vibrational bonds in seconds. Coherent anti-Stokes Raman (CARS) microscopy and stimulated Raman loss (SRL) microscopy are orders of magnitude more efficient than Raman spectroscopy, and are able to acquire high quality chemically-specific images in seconds. We discuss the benefits and limitations of all techniques, with particular emphasis on applications in biomedicine—both in vivo (using fiber endoscopes) and in vitro (in optical microscopes). [ABSTRACT FROM AUTHOR]

Tip-enhanced optical spectroscopy is an approach that holds a good deal of promise for the nanoscale characterisation of matter. Tip-enhanced Raman spectroscopy (TERS) has been demonstrated on a variety of samples: inorganic, organic and biological. Imaging using TERS has been shown for carbon nanotubes due to their high scattering efficiency. There are a number of compelling motivations to consider alternative approaches for biological samples; most importantly, the potential for heat damage of biomolecules and long acquisition times. These issues may be addressed through the development of tip-enhanced coherent anti-Stokes Raman scattering microscopy. [ABSTRACT FROM AUTHOR]

Myoblasts are muscle derived mesenchymal stem cell progenitors that have great potential for use in regenerative medicine, especially for cardiomyogenesis grafts and intracardiac cell transplantation. To utilise such cells for pre-clinical and clinical applications, and especially for personalized medicine, it is essential to generate a synchronised, homogenous, population of cells that display phenotypic and genotypic homogeneity within a population of cells. We demonstrate that the biomarker-free technique of dielectrophoresis (DEP) can be used to discriminate cells between stages of differentiation in the C2C12 myoblast multipotent mouse model. Terminally differentiated myotubes were separated from C2C12 myoblasts to better than 96% purity, a result validated by flow cytometry and Western blotting. To determine the extent to which cell membrane capacitance, rather than cell size, determined the DEP response of a cell, C2C12 myoblasts were co-cultured with GFP-expressing MRC-5 fibroblasts of comparable size distributions (mean diameter ∼10 μm). A DEP sorting efficiency greater than 98% was achieved for these two cell types, a result concluded to arise from the fibroblasts possessing a larger membrane capacitance than the myoblasts. It is currently assumed that differences in membrane capacitance primarily reflect differences in the extent of folding or surface features of the membrane. However, our finding by Raman spectroscopy that the fibroblast membranes contained a smaller proportion of saturated lipids than those of the myoblasts suggests that the membrane chemistry should also be taken into account. [ABSTRACT FROM AUTHOR]